TY - JOUR
T1 - 3-HBP
T2 - A Three-Level Hidden Bayesian Link Prediction Model in Social Networks
AU - Xiao, Yunpeng
AU - Li, Xixi
AU - Wang, Haohan
AU - Xu, Ming
AU - Liu, Yanbing
N1 - Manuscript received August 7, 2017; revised January 25, 2018; accepted February 27, 2018. Date of publication March 20, 2018; date of current version May 25, 2018. This work was supported in part by the National Key Basic Research Program (973 program) of China under Grant 2013CB329606, in part by the National Science Foundation of China under Grant 61772098, in part by the Chongqing Youth Innovative Talent Project under Grant cstc2013kjrc-qnrc40004, in part by the Science and Technology Research Program of the Chongqing Municipal Education Committee under Grant KJ1500425, in part by the Chongqing Science and Technology Commission Project under Grant cstc2017jcyjAX0099, and in part by the Chongqing Key Research and Development Project under Grant cstc2017zdcy-zdyf0299 and Grant cstc2017zdcy-zdyf0436. (Corresponding author: Yunpeng Xiao.) Y. Xiao, X. Li, and Y. Liu are with the Chongqing Engineering Laboratory of Internet and Information Security, Chongqing University of Posts and Telecommunications, Chongqing 400065, China (e-mail: [email protected]; [email protected]; [email protected]).
PY - 2018/6
Y1 - 2018/6
N2 - In social networks, link establishment among the users is affected by complex factors. In this paper, we try to investigate the internal and external factors that affect the formation of links and propose a three-level hidden Bayesian link prediction model by integrating the user behavior as well as user relationships to link prediction. First, based on the user multiple interest characteristics, a latent Dirichlet allocation (LDA) traditional text modeling method is applied into user behavior modeling. Taking the advantage of LDA topic model in dealing with the problem of polysemy and synonym, we can mine user latent interest distribution and analyze the effects of internal driving factors. Second, owing to the power-law characteristics of user behavior, LDA is improved by Gaussian weighting. In this way, the negative impact of the interest distribution to the high-frequency users can be reduced and the expression ability of interests can be enhanced. Furthermore, taking the impact of common neighbor dependencies in link establishment, the model can be extended with hidden naive Bayesian algorithm. By quantifying the dependencies between common neighbors, we can analyze the effects of external driving factors and combine internal driving factors to link prediction. Experimental results indicate that the model can not only mine user latent interest distribution but also can improve the performance of link prediction effectively.
AB - In social networks, link establishment among the users is affected by complex factors. In this paper, we try to investigate the internal and external factors that affect the formation of links and propose a three-level hidden Bayesian link prediction model by integrating the user behavior as well as user relationships to link prediction. First, based on the user multiple interest characteristics, a latent Dirichlet allocation (LDA) traditional text modeling method is applied into user behavior modeling. Taking the advantage of LDA topic model in dealing with the problem of polysemy and synonym, we can mine user latent interest distribution and analyze the effects of internal driving factors. Second, owing to the power-law characteristics of user behavior, LDA is improved by Gaussian weighting. In this way, the negative impact of the interest distribution to the high-frequency users can be reduced and the expression ability of interests can be enhanced. Furthermore, taking the impact of common neighbor dependencies in link establishment, the model can be extended with hidden naive Bayesian algorithm. By quantifying the dependencies between common neighbors, we can analyze the effects of external driving factors and combine internal driving factors to link prediction. Experimental results indicate that the model can not only mine user latent interest distribution but also can improve the performance of link prediction effectively.
KW - Hidden naive Bayes
KW - latent Dirichlet allocation (LDA)
KW - latent interest
KW - link prediction
KW - social networks
UR - http://www.scopus.com/inward/record.url?scp=85044326919&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85044326919&partnerID=8YFLogxK
U2 - 10.1109/TCSS.2018.2812721
DO - 10.1109/TCSS.2018.2812721
M3 - Article
AN - SCOPUS:85044326919
SN - 2329-924X
VL - 5
SP - 430
EP - 443
JO - IEEE Transactions on Computational Social Systems
JF - IEEE Transactions on Computational Social Systems
IS - 2
ER -